This application for "Neurotechnology Research, Development, and Enhancement" (PA-04-006) proposes the development of innovative technologies, methodologies, and instrumentation to advance our understanding of neural control mechanisms of muscle force production through a non-invasive means of recording neuronal firing patterns. The project will develop an automatic system to accurately and quickly decompose the surface Electromyographic (sEMG) signal into its constituent action potentials and provide the timing of the firings of concurrently active motor units. The goal is to achieve an accuracy of >85% in the automatic mode and >96% with the assistance of an interactive editor. This information will enable a wide range of studies to investigate the workings of the healthy and diseased neuromuscular system by simply placing a sensor above a muscle with no assault to the CNS. The sEMG Decomposition System will replace existing technology that relies on invasive procedures to detect the EMG signal through needle or fine-wire electrodes. The proposed work includes: 1) mathematical modeling and empirical studies to develop a sEMG electrode array that maximizes shape differences of motor unit firings and thereby facilitates sEMG signal decomposition; 2) algorithm development using artificial intelligence technology of our own design combined with Principal Component Analysis techniques; and 3) data acquisition/processing software and hardware to build a portable prototype surface decomposition system. Performance testing of the system will be conducted using data collection experiments to ensure that the system is comparable in motor unit yield, processing speed, and accuracy to the current state-of-the art indwelling decomposition system. We will also prove that the signal decomposition is performed correctly by decomposing two separately collected signals and matching the results. A dissemination plan is included to make this technology available to the Motor Control community. Commercialization will be realized through Altec Inc. This technology will enable researchers in the fields of Motor Control, Aging, Exercise Physiology, Space Medicine, and Ergonomics, where it is of interest to understand how the CNS controls muscles, and how that control is altered as a consequence of aging, exercise, exposure to microgravity, fatigue, and excessive and prolonged force production. It will be useful to clinicians for assessing the degree of dysfunction in upper motoneuron diseases such as Cerebral Palsy, Parkinson's Disease, ALS, Stroke, and other disorders. [unreadable] [unreadable] [unreadable] [unreadable]